Currently, imaging data is captured at an image capturing location during a patient examination. Typically, a number of data sets or studies are created by the image acquisition device. These studies will later be reviewed on demand at a review station, likely having a location that is not yet determined. Accordingly, the captured data is stored in a central server location such as a picture archiving communication system (PACS) until it is requested by a reviewer.
In the field of medical imaging, modern techniques have created large data sets that can be cumbersome to transfer and store. Specialists including cardiologists need motion pictures at thirty or more frames per second. Each frame could be several megabytes or more in size. X-ray Angiography studies can average around one gigabyte or more.
When a physician is ready to review the studies, the physician will go to a review station to read and analyze the studies. Physicians may require that the study be available for reading immediately upon demand. Because of the large size of the data sets and the limited speed of hospital networks, simply retrieving the studies on demand may yield unacceptably long waiting times for physicians.
Several solutions have been proposed to the aforementioned problem. One solution is for the central data server to auto re-route the collected studies to all diagnostic stations. While this solution makes the images immediately available to physicians, it causes excessive network traffic and consumes a large amount of disk space.
Another solution created to solve this problem has been to automatically route the studies to a small set of review stations based on predictive algorithms. This solution also has drawbacks. For instance, if a physician selects a review station that was not predicted by the predictive algorithm, the physician may be required to wait an excessive amount of time for all of the studies to download. Alternatively, the physician may be forced to find a review station where the predictive algorithm routed the studies. Additionally, those stations selected by the predictive algorithm lose valuable storage space.
Accordingly, a solution is needed for making the large data sets available to the physician that does not require predicting physician location. A solution is also needed that does not auto-route all data sets to all review stations, thus creating excessive network traffic and consuming excessive memory resources.